Composing and updating verb argument expectations: a distributional semantic model

Authors:
Alessandro Lenci
Affiliations:
University of Pisa, Pisa (Italy)
Venue:
CMCL '11 Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics
Year:
2011

Citing 8
Cited 1

Automatic retrieval and clustering of similar words

COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Similarity of Semantic Relations

Computational Linguistics
Dependency-Based Construction of Semantic Space Models

Computational Linguistics
A structured vector space model for word meaning in context

EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Tensor Decompositions and Applications

SIAM Review
From frequency to meaning: vector space models of semantics

Journal of Artificial Intelligence Research
Distributional memory: A general framework for corpus-based semantics

Computational Linguistics
A flexible, corpus-driven model of regular and inverse selectional preferences

Computational Linguistics

Modeling covert event retrieval in logical metonymy: probabilistic and distributional accounts

CMCL '12 Proceedings of the 3rd Workshop on Cognitive Modeling and Computational Linguistics

Quantified Score

Hi-index	0.00

Visualization

Abstract

The aim of this paper is to present a computational model of the dynamic composition and update of verb argument expectations using Distributional Memory, a state-of-the-art framework for distributional semantics. The experimental results conducted on psycholinguistic data sets show that the model is able to successfully predict the changes on the patient argument thematic fit produced by different types of verb agents.